from __future__ import annotations






class 		_lowerCamelCase		:
    def __init__(	self			,			lowerCAmelCase )							->       str:
     SCREAMING_SNAKE_CASE__: int=   TypeError(
         '''Matrices must be formed from a list of zero or more lists containing at '''
         '''least one and the same number of values, each of which must be of type '''
         '''int or float.''' )
     if len(lowerCAmelCase ) != 0:
      SCREAMING_SNAKE_CASE__: List[str]=   len(rows[0] )
      if cols == 0:
       raise error
      for row in rows:
       if len(lowerCAmelCase ) != cols:
        raise error
       for value in row:
        if not isinstance(lowerCAmelCase			,			(int, float) ):
         raise error
      SCREAMING_SNAKE_CASE__: Optional[Any]=   rows
     else:
      SCREAMING_SNAKE_CASE__: List[str]=   []
    def     UpperCamelCase_					(	self )							->       list[list[int]]:
     return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
    @property
    def     UpperCamelCase_					(	self )							->       int:
     return len(self.rows )
    @property
    def     UpperCamelCase_					(	self )							->       int:
     return len(self.rows[0] )
    @property
    def     UpperCamelCase_					(	self )							->       tuple[int, int]:
     return (self.num_rows, self.num_columns)
    @property
    def     UpperCamelCase_					(	self )							->       bool:
     return self.order[0] == self.order[1]
    def     UpperCamelCase_					(	self )							->       Matrix:
     SCREAMING_SNAKE_CASE__: Optional[int]=   [
         [0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
         for row_num in range(self.num_rows )
     ]
     return Matrix(lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       int:
     if not self.is_square:
      return 0
     if self.order == (0, 0):
      return 1
     if self.order == (1, 1):
      return int(self.rows[0][0] )
     if self.order == (2, 2):
      return int(
          (self.rows[0][0] * self.rows[1][1])
          - (self.rows[0][1] * self.rows[1][0]) )
     else:
      return sum(
          self.rows[0][column] * self.cofactors().rows[0][column]
          for column in range(self.num_columns ) )
    def     UpperCamelCase_					(	self )							->       bool:
     return bool(self.determinant() )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       int:
     SCREAMING_SNAKE_CASE__: Tuple=   [
         [
             self.rows[other_row][other_column]
             for other_column in range(self.num_columns )
             if other_column != column
         ]
         for other_row in range(self.num_rows )
         if other_row != row
     ]
     return Matrix(lowerCAmelCase ).determinant()
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       int:
     if (row + column) % 2 == 0:
      return self.get_minor(lowerCAmelCase			,			lowerCAmelCase )
     return -1 * self.get_minor(lowerCAmelCase			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Matrix:
     return Matrix(
         [
             [self.get_minor(lowerCAmelCase			,			lowerCAmelCase ) for column in range(self.num_columns )]
             for row in range(self.num_rows )
         ] )
    def     UpperCamelCase_					(	self )							->       Matrix:
     return Matrix(
         [
             [
                 self.minors().rows[row][column]
                 if (row + column) % 2 == 0
                 else self.minors().rows[row][column] * -1
                 for column in range(self.minors().num_columns )
             ]
             for row in range(self.minors().num_rows )
         ] )
    def     UpperCamelCase_					(	self )							->       Matrix:
     SCREAMING_SNAKE_CASE__: int=   [
         [self.cofactors().rows[column][row] for column in range(self.num_columns )]
         for row in range(self.num_rows )
     ]
     return Matrix(lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Matrix:
     SCREAMING_SNAKE_CASE__: int=   self.determinant()
     if not determinant:
      raise TypeError('''Only matrices with a non-zero determinant have an inverse''' )
     return self.adjugate() * (1 / determinant)
    def __repr__(	self )							->       str:
     return str(self.rows )
    def __str__(	self )							->       str:
     if self.num_rows == 0:
      return "[]"
     if self.num_rows == 1:
      return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
     return (
         "["
         + "\n ".join(
             [
                 '''[''' + '''. '''.join([str(lowerCAmelCase ) for value in row] ) + '''.]'''
                 for row in self.rows
             ] )
         + "]"
     )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None )							->       None:
     SCREAMING_SNAKE_CASE__: Any=   TypeError('''Row must be a list containing all ints and/or floats''' )
     if not isinstance(lowerCAmelCase			,			lowerCAmelCase ):
      raise type_error
     for value in row:
      if not isinstance(lowerCAmelCase			,			(int, float) ):
       raise type_error
     if len(lowerCAmelCase ) != self.num_columns:
      raise ValueError(
          '''Row must be equal in length to the other rows in the matrix''' )
     if position is None:
      self.rows.append(lowerCAmelCase )
     else:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.rows[0:position] + [row] + self.rows[position:]
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None )							->       None:
     SCREAMING_SNAKE_CASE__: int=   TypeError(
         '''Column must be a list containing all ints and/or floats''' )
     if not isinstance(lowerCAmelCase			,			lowerCAmelCase ):
      raise type_error
     for value in column:
      if not isinstance(lowerCAmelCase			,			(int, float) ):
       raise type_error
     if len(lowerCAmelCase ) != self.num_rows:
      raise ValueError(
          '''Column must be equal in length to the other columns in the matrix''' )
     if position is None:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   [self.rows[i] + [column[i]] for i in range(self.num_rows )]
     else:
      SCREAMING_SNAKE_CASE__: List[str]=   [
          self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
          for i in range(self.num_rows )
      ]
    def __eq__(	self			,			lowerCAmelCase )							->       bool:
     if not isinstance(lowerCAmelCase			,			lowerCAmelCase ):
      return NotImplemented
     return self.rows == other.rows
    def __ne__(	self			,			lowerCAmelCase )							->       bool:
     return not self == other
    def __neg__(	self )							->       Matrix:
     return self * -1
    def __add__(	self			,			lowerCAmelCase )							->       Matrix:
     if self.order != other.order:
      raise ValueError('''Addition requires matrices of the same order''' )
     return Matrix(
         [
             [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
             for i in range(self.num_rows )
         ] )
    def __sub__(	self			,			lowerCAmelCase )							->       Matrix:
     if self.order != other.order:
      raise ValueError('''Subtraction requires matrices of the same order''' )
     return Matrix(
         [
             [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
             for i in range(self.num_rows )
         ] )
    def __mul__(	self			,			lowerCAmelCase )							->       Matrix:
     if isinstance(lowerCAmelCase			,			(int, float) ):
      return Matrix(
          [[int(element * other ) for element in row] for row in self.rows] )
     elif isinstance(lowerCAmelCase			,			lowerCAmelCase ):
      if self.num_columns != other.num_rows:
       raise ValueError(
           '''The number of columns in the first matrix must '''
           '''be equal to the number of rows in the second''' )
      return Matrix(
          [
              [Matrix.dot_product(lowerCAmelCase			,			lowerCAmelCase ) for column in other.columns()]
              for row in self.rows
          ] )
     else:
      raise TypeError(
          '''A Matrix can only be multiplied by an int, float, or another matrix''' )
    def __pow__(	self			,			lowerCAmelCase )							->       Matrix:
     if not isinstance(lowerCAmelCase			,			lowerCAmelCase ):
      raise TypeError('''A Matrix can only be raised to the power of an int''' )
     if not self.is_square:
      raise ValueError('''Only square matrices can be raised to a power''' )
     if other == 0:
      return self.identity()
     if other < 0:
      if self.is_invertable():
       return self.inverse() ** (-other)
      raise ValueError(
          '''Only invertable matrices can be raised to a negative power''' )
     SCREAMING_SNAKE_CASE__: str=   self
     for _ in range(other - 1 ):
      result *= self
     return result



    @classmethod
    def     UpperCamelCase_					(	cls			,			lowerCAmelCase			,			lowerCAmelCase )							->       int:
     return sum(row[i] * column[i] for i in range(len(lowerCAmelCase ) ) )


if __name__ == "__main__":
      import doctest

      doctest.testmod()





"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}







class    __a						( __snake_case							):
				lowerCamelCase     :       Union[str, Any]			       ='open-llama'







				def __init__( self      ,	UpperCAmelCase=10_0000      ,	UpperCAmelCase=4096      ,	UpperCAmelCase=1_1008      ,	UpperCAmelCase=32      ,	UpperCAmelCase=32      ,	UpperCAmelCase="silu"      ,	UpperCAmelCase=2048      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=1E-6      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0      ,	UpperCAmelCase=1      ,	UpperCAmelCase=2      ,	UpperCAmelCase=False      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase=None      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			vocab_size
					lowerCAmelCase_      							=			max_position_embeddings
					lowerCAmelCase_      							=			hidden_size
					lowerCAmelCase_      							=			intermediate_size
					lowerCAmelCase_      							=			num_hidden_layers
					lowerCAmelCase_      							=			num_attention_heads
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			rms_norm_eps
					lowerCAmelCase_      							=			use_cache
					lowerCAmelCase_      							=			kwargs.pop(
					    '''use_memorry_efficient_attention'''      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			hidden_dropout_prob
					lowerCAmelCase_      							=			attention_dropout_prob
					lowerCAmelCase_      							=			use_stable_embedding
					lowerCAmelCase_      							=			shared_input_output_embedding
					lowerCAmelCase_      							=			rope_scaling
					self._rope_scaling_validation()

					super().__init__(
					    pad_token_id=UpperCAmelCase      ,	bos_token_id=UpperCAmelCase      ,	eos_token_id=UpperCAmelCase      ,	tie_word_embeddings=UpperCAmelCase      ,	**UpperCAmelCase      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.rope_scaling is None:
						return

					if not isinstance(self.rope_scaling      ,	UpperCAmelCase   ) or len(self.rope_scaling   ) != 2:
						raise ValueError(
						    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
						    F"""got {self.rope_scaling}"""   )
					lowerCAmelCase_      							=			self.rope_scaling.get('''type'''      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			self.rope_scaling.get('''factor'''      ,	UpperCAmelCase   )
					if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""   )
					if rope_scaling_factor is None or not isinstance(UpperCAmelCase      ,	UpperCAmelCase   ) or rope_scaling_factor <= 1.0:
						raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""   )